Opening the Black Box: Exploring Temporal Pattern of Type 2 Diabetes Complications in Patient Clustering Using Association Rules and Hidden Variable Discovery

Yousefi, Leila; Swift, Stephen; Arzoky, Mahir; Sacchi, Lucia; Chiovato, Luca; Tucker, Allan

doi:10.1109/cbms.2019.00048

Cited by 8 publications

(11 citation statements)

References 14 publications

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“…Another study in Reference 3 have primarily concentrated on the clustering approach based on the latent variable to personalise the patients. That is consistent with the very current work in Reference 4 , which also provided a comparison methodology to evaluate the discovered latent variable clusters by using a combination of supervised learning such as clustering and TARs among the binary complications. Hence, Reference 4 found similar clusters to those obtained in Reference 3 .…”

Section: Introductionsupporting

confidence: 89%

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Opening the black box: Personalizing type 2 diabetes patients based on their latent phenotype and temporal associated complication rules

Yousefi

Swift

Arzoky

et al. 2020

Computational Intelligence

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It is widely considered that approximately 10% of the population suffers from type 2 diabetes. Unfortunately, the impact of this disease is underestimated. Patient's mortality often occurs due to complications caused by the disease and not the disease itself. Many techniques utilized in modeling diseases are often in the form of a “black box” where the internal workings and complexities are extremely difficult to understand, both from practitioners' and patients' perspective. In this work, we address this issue and present an informative model/pattern, known as a “latent phenotype,” with an aim to capture the complexities of the associated complications' over time. We further extend this idea by using a combination of temporal association rule mining and unsupervised learning in order to find explainable subgroups of patients with more personalized prediction. Our extensive findings show how uncovering the latent phenotype aids in distinguishing the disparities among subgroups of patients based on their complications patterns. We gain insight into how best to enhance the prediction performance and reduce bias in the models applied using uncertainty in the patients' data.

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Section: Introductionsupporting

confidence: 89%

“…Preliminary experiments obtained in Reference 4 showed that it is possible to find subgroups of patients only based on their latent phenotype. Nevertheless, the techniques used in these investigations were not validated for interpreting each subgroup to enhance the prediction of the associated complications.…”

Section: Introductionmentioning

confidence: 99%

Opening the black box: Personalizing type 2 diabetes patients based on their latent phenotype and temporal associated complication rules

Yousefi

Swift

Arzoky

et al. 2020

Computational Intelligence

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“…The data for this study consists of pre-diagnosed T2DM patients aged 25 as the main focus is to predict only one complication at time [1] .…”

Section: Data Sourcementioning

confidence: 99%

“…In our previous work, an intuitive stepwise method to learn the latent effects was developed based upon the IC* algorithm, while using a Pair-Sampling re-balancing method [23]. In [24][25][26], patients were clustered into different sub-groups. In each sub-group, they shared a similar profile of observed risk factors, without taking account of the cluster decision making process.…”

Section: Introductionmentioning

confidence: 99%

Identifying Latent Variables in Dynamic Bayesian Networks with Bootstrapping Applied to Type 2 Diabetes Complication Prediction

Yousefi

Al-Luhaybi

Sacchi

et al. 2020

Preprint

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Background: Type 2 Diabetes is a chronic disease with an onset that is commonly associated with multiple life-threatening co morbidities (complications). Early prediction of diabetic complications while discovering the behaviour of associated aggressive risk factors can reduce the patients’ suffering time. Therefore, models of the time series diabetic data (which are often imbalanced, incomplete and involve complex interactions) are needed to better manage diabetic complications. Aims: The aim of this work is to both deals with imbalanced clinical data using a bootstrapping approach, whilst determining the precise position of latent variables within probabilistic networks generated from the observations. The main motivation behind this paper is to stratify patient groups by means of latent variables to discover how complications in diabetes interact. Methods: We propose a time series bootstrapping method for building Dynamic Bayesian Networks that includes hidden / latent variables, applied to a case for predicting T2DM complications. A combination of the IC* algorithm on time series bootstrapped data is utilized to identify the latent variables within Bayesian model. Then, an exploration of inference methods assessed the influences of these latent variables. Results: Our promising findings show how this targeted use of latent variables improves prediction accuracy, specificity, and sensitivity over standard approaches as well as aiding the understanding of relationships between these latent variable sand disease complications / risk factors. The contribution of this paper compared to the previous papers in which time series bootstrapping is used for re-balancing the data and providing confidence in the prediction results. Conclusion: Our results showed that our re-balancing approach by the use of Time Series bootstrapping method for an unequal number of time series visits demonstrated an improvement in the prediction performance. Additionally, the most highlighted contribution of this paper gained insight by interpreting the latent states (looking at the associated distributions of complications), which led to a better understanding of risk factors and patient-specific interventions: here the fact that the latent variable demonstrated that a patient falls into a sub-group that is hypertensive but not suffering from retinopathy.

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“…The extraction and arrangement process are the part of data mining. The knowledge discovery is the important in terms of data acquisition and data exploration in terms of information discovery [6][7][8][9]. The design and information of the structural goal is to be capable in finding the latest trends and technological aspects in terms of different aspects of experimentation and analysis of all the empirical and calculative way.…”

Section: Introductionmentioning

confidence: 99%

An Efficient K-Means Method Based on Centroid Handling for the Similarity Estimation

Ladha,

Pippal

2019

IJEAT

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The main aim of this paper is to handle centroid calculation in k-means efficiently. So that the distance estimation will be more accurate and prominent results will be fetched in terms of clustering. For this PIMA database has been considered. Data preprocessing has been performed for the unwanted data removal in terms of missing values. Then centroid initialization has been performed based on centroid tuning and randomization. For distance estimation Euclidean, Pearson Coefficient, Chebyshev and Canberra algorithms has been used. In this paper the evaluation has been performed based on the computational time analysis. The time calculation has been performed on different random sets. It is found to be prominent in all the cases considering the variations in all aspects of distance and population

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Opening the Black Box: Exploring Temporal Pattern of Type 2 Diabetes Complications in Patient Clustering Using Association Rules and Hidden Variable Discovery

Cited by 8 publications

References 14 publications

Opening the black box: Personalizing type 2 diabetes patients based on their latent phenotype and temporal associated complication rules

Opening the black box: Personalizing type 2 diabetes patients based on their latent phenotype and temporal associated complication rules

Identifying Latent Variables in Dynamic Bayesian Networks with Bootstrapping Applied to Type 2 Diabetes Complication Prediction

An Efficient K-Means Method Based on Centroid Handling for the Similarity Estimation

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